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Artificial Intelligence in Transportation Safety and Traffic Management
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Artificial Intelligence in Transportation Safety and Traffic Management

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Transportation and Future Mobility".

Deadline for manuscript submissions: 20 February 2025 | Viewed by 6382

Special Issue Editor

Dr. Yuanbo Xu

E-Mail Website
Guest Editor
College of Computer Science and Technology, Jilin University, Changchun 130012, China
Interests: machine learning; artificial intelligence; abnormal data monitoring; smart city

Special Issue Information

Dear Colleagues,

A standardized and intelligent traffic system is necessary to improve transportation safety and traffic management efficiency. Artificial intelligence can predict and solve various types of problems such as intersection signal control, traffic scheduling, and vehicle motion planning, so as to achieve the regulation of the traffic system. This Special Issue aims to share innovative ideas on how artificial intelligence can improve transportation safety and traffic management efficiency.

This Special Issue therefore welcomes original application-focused research and investigations using AI to predict and solve traffic problems, such as graph neural networks, graph attention networks, and so on. It aims to promote communication and interaction between researchers in different fields. We invite high quality original research articles as well as review articles.

Dr. Yuanbo Xu
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • intelligent traffic
  • artificial intelligence
  • transportation safety
  • traffic prediction
  • graph neural network
  • graph attention network
  • machine learning

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Published Papers (6 papers)

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Research

15 pages, 416 KiB  
Article
HGTMFS: A Hypergraph Transformer Framework for Multimodal Summarization
by Ming Lu, Xinxi Lu and Xiaoming Zhang
Appl. Sci. 2024, 14(20), 9563; https://doi.org/10.3390/app14209563 - 20 Oct 2024
Viewed by 673
Abstract
Multimodal summarization, a rapidly evolving field within multimodal learning, focuses on generating cohesive summaries by integrating information from diverse modalities, such as text and images. Unlike traditional unimodal summarization, multimodal summarization presents unique challenges, particularly in capturing fine-grained interactions between modalities. Current models [...] Read more.
Multimodal summarization, a rapidly evolving field within multimodal learning, focuses on generating cohesive summaries by integrating information from diverse modalities, such as text and images. Unlike traditional unimodal summarization, multimodal summarization presents unique challenges, particularly in capturing fine-grained interactions between modalities. Current models often fail to account for complex cross-modal interactions, leading to suboptimal performance and an over-reliance on one modality. To address these issues, we propose a novel framework, hypergraph transformer-based multimodal summarization (HGTMFS), designed to model high-order relationships across modalities. HGTMFS constructs a hypergraph that incorporates both textual and visual nodes and leverages transformer mechanisms to propagate information within the hypergraph. This approach enables the efficient exchange of multimodal data and improves the integration of fine-grained semantic relationships. Experimental results on several benchmark datasets demonstrate that HGTMFS outperforms state-of-the-art methods in multimodal summarization. Full article
(This article belongs to the Special Issue Artificial Intelligence in Transportation Safety and Traffic Management)
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21 pages, 4510 KiB  
Article
Pedestrian Trajectory Prediction in Crowded Environments Using Social Attention Graph Neural Networks
by Mengya Zong, Yuchen Chang, Yutian Dang and Kaiping Wang
Appl. Sci. 2024, 14(20), 9349; https://doi.org/10.3390/app14209349 - 14 Oct 2024
Viewed by 1012
Abstract
Trajectory prediction is a key component in the development of applications such as mixed urban traffic management and public safety. Traditional models have struggled with the complexity of modeling dynamic crowd interactions, the intricacies of spatiotemporal dependencies, and environmental constraints. Addressing these challenges, [...] Read more.
Trajectory prediction is a key component in the development of applications such as mixed urban traffic management and public safety. Traditional models have struggled with the complexity of modeling dynamic crowd interactions, the intricacies of spatiotemporal dependencies, and environmental constraints. Addressing these challenges, this paper introduces the innovative Social Attention Graph Neural Network (SA-GAT) framework. Utilizing Long Short-Term Memory (LSTM) networks, SA-GAT encodes pedestrian trajectory data to extract temporal correlations, while Graph Attention Networks (GAT) are employed to precisely capture the subtle interactions among pedestrians. The SA-GAT framework boosts its predictive accuracy with two key innovations. First, it features a Scene Potential Module that utilizes a Scene Tensor to dynamically capture the interplay between crowds and their environment. Second, it incorporates a Transition Intention Module with a Transition Tensor, which interprets latent transfer probabilities from trajectory data to reveal pedestrians’ implicit intentions at specific locations. Based on AnyLogic modeling of the metro station on Line 10 of Chengdu Shuangliu Airport, China, numerical studies reveal that the SA-GAT model achieves a substantial reduction in ADE and FDE metrics by 34.22% and 38.04% compared to baseline models. Full article
(This article belongs to the Special Issue Artificial Intelligence in Transportation Safety and Traffic Management)
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11 pages, 428 KiB  
Article
A Modality-Enhanced Multi-Channel Attention Network for Multi-Modal Dialogue Summarization
by Ming Lu, Yang Liu and Xiaoming Zhang
Appl. Sci. 2024, 14(20), 9184; https://doi.org/10.3390/app14209184 - 10 Oct 2024
Viewed by 474
Abstract
Integrating multi-modal data in natural language processing has opened new pathways for the enhancement of dialogue summarization. However, existing models often struggle to effectively synthesize textual, auditory, and visual inputs. This paper introduces a Modality-Enhanced Multi-Channel Attention Network (MEMA), a novel approach designed [...] Read more.
Integrating multi-modal data in natural language processing has opened new pathways for the enhancement of dialogue summarization. However, existing models often struggle to effectively synthesize textual, auditory, and visual inputs. This paper introduces a Modality-Enhanced Multi-Channel Attention Network (MEMA), a novel approach designed to optimize the integration and interaction of diverse modalities for dialogue summarization. MEMA leverages symmetrical embedding strategies to balance the integrity and distinctiveness of each modality, ensuring a harmonious interaction within the unified architecture. By maintaining symmetry in the processing flow, MEMA enhances the contextual richness and coherence of the generated summaries. Our model demonstrates superior performance on the Multi-modal Dialogue Summarization (MDS) dataset, particularly in generating contextually enriched abstract summaries. The results underscore MEMA’s potential to transform dialogue summarization by providing a more symmetrical and integrated understanding of multi-modal interactions, bridging the gap in multi-modal data processing, and setting a new standard for future summarization tasks. Full article
(This article belongs to the Special Issue Artificial Intelligence in Transportation Safety and Traffic Management)
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21 pages, 3932 KiB  
Article
Multi-Step Passenger Flow Prediction for Urban Metro System Based on Spatial-Temporal Graph Neural Network
by Yuchen Chang, Mengya Zong, Yutian Dang and Kaiping Wang
Appl. Sci. 2024, 14(18), 8121; https://doi.org/10.3390/app14188121 - 10 Sep 2024
Viewed by 1069
Abstract
Efficient operation of urban metro systems depends on accurate passenger flow predictions, a task complicated by intricate spatiotemporal correlations. This paper introduces a novel spatiotemporal graph neural network (STGNN) designed explicitly for predicting multistep passenger flow within metro stations. In the spatial dimension, [...] Read more.
Efficient operation of urban metro systems depends on accurate passenger flow predictions, a task complicated by intricate spatiotemporal correlations. This paper introduces a novel spatiotemporal graph neural network (STGNN) designed explicitly for predicting multistep passenger flow within metro stations. In the spatial dimension, previous research primarily focuses on local spatial dependencies, struggling to capture implicit global information. We propose a spatial modeling module that leverages a dynamic global attention network (DGAN) to capture dynamic global information from all-pair interactions, intricately fusing prior knowledge from the input graph with a graph convolutional network. In the temporal dimension, we design a temporal modeling module tailored to navigate the challenges of both long-term and recent-term temporal passenger flow patterns. This module consists of series decomposition blocks and locality-aware sparse attention (LSA) blocks to incorporate multiple local contexts and reduce computational complexities in long sequence modeling. Experiments conducted on both simulated and real-world datasets validate the exceptional predictive performance of our proposed model. Full article
(This article belongs to the Special Issue Artificial Intelligence in Transportation Safety and Traffic Management)
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15 pages, 3686 KiB  
Article
CPF-UNet: A Dual-Path U-Net Structure for Semantic Segmentation of Panoramic Surround-View Images
by Qiqing Sun and Feng Qu
Appl. Sci. 2024, 14(13), 5473; https://doi.org/10.3390/app14135473 - 24 Jun 2024
Cited by 2 | Viewed by 1076
Abstract
In this study, we propose a dual-stream UNet neural network architecture design named CPF-UNet, specifically designed for efficient semantic pixel-level segmentation tasks. This architecture cleverly extends the basic structure of the original UNet, mainly through the addition of a unique attention-guided branch in [...] Read more.
In this study, we propose a dual-stream UNet neural network architecture design named CPF-UNet, specifically designed for efficient semantic pixel-level segmentation tasks. This architecture cleverly extends the basic structure of the original UNet, mainly through the addition of a unique attention-guided branch in the encoder part, aiming to enhance the model’s ability to comprehensively capture and deeply fuse contextual information. The uniqueness of CPF-UNet lies in its dual-path mechanism, which differs from the dense connectivity strategy adopted in networks such as UNet++. The dual-path structure in this study can effectively integrate deep and shallow features without relying excessively on dense connections, achieving a balanced processing of image details and overall semantic information. Experiments have shown that CPF-UNet not only slightly surpasses the segmentation accuracy of UNet++, but also significantly reduces the number of model parameters, thereby improving inference efficiency. We conducted a detailed comparative analysis, evaluating the performance of CPF-UNet against existing UNet++ and other corresponding methods on the same benchmark. The results indicate that CPF-UNet achieves a more ideal balance between accuracy and parameter quantity, two key performance indicators. Full article
(This article belongs to the Special Issue Artificial Intelligence in Transportation Safety and Traffic Management)
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19 pages, 2082 KiB  
Article
Personalized Privacy Protection Based on Space Grid in Mobile Crowdsensing
by Hengfei Gao, Ziqing Zhang and Hongwei Zhao
Appl. Sci. 2023, 13(23), 12696; https://doi.org/10.3390/app132312696 - 27 Nov 2023
Viewed by 931
Abstract
The rapid proliferation of handheld intelligent devices and the advent of 5G technology have brought about convenient and fast services for people. In perception-oriented application services, participating users will upload sensitive mobile data in order to obtain benefits. While devising privacy protection strategies [...] Read more.
The rapid proliferation of handheld intelligent devices and the advent of 5G technology have brought about convenient and fast services for people. In perception-oriented application services, participating users will upload sensitive mobile data in order to obtain benefits. While devising privacy protection strategies to ensure data security, it is crucial to accomplish task perception related to data collection to the fullest extent possible. To address this challenge, this paper proposes a personalized data privacy protection algorithm based on an adaptive dynamic adjustment grid and the minimum wage task allocation strategy. According to the different levels of users’ needs for privacy protection, combined with the privacy budget allocation strategy, we design a different-level differential privacy protection mechanism and consider the reward mechanism in task allocation to balance the effectiveness and security of the location data uploaded by users. Experiments show that the strategy proposed in this paper can not only protect the data but also enable users to freely choose the level of privacy protection. Full article
(This article belongs to the Special Issue Artificial Intelligence in Transportation Safety and Traffic Management)
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